def test_overlay():
im = mh.demos.load('luispedro', as_grey=1)
im = mh.stretch(im)
assert np.all(mh.overlay(im).max(2) == im)
edges = mh.sobel(im)
im3 = mh.overlay(im, green=edges)
assert np.all(im3[:,:,0] == im)
assert np.all(im3[:,:,2] == im)
assert np.all(im3[:,:,1] >= im )
def test_overlay():
im = mh.demos.load('luispedro', as_grey=1)
im = mh.stretch(im)
assert np.all(mh.overlay(im).max(2) == im)
edges = mh.sobel(im)
im3 = mh.overlay(im, green=edges)
assert np.all(im3[:, :, 0] == im)
assert np.all(im3[:, :, 2] == im)
assert np.all(im3[:, :, 1] >= im)
def main():
Image_orig = mh.imread("./cameraman.tif")
if Image_orig.ndim == 3:
Image_orig_grey = mh.colors.rgb2grey(Image_orig)
else:
Image_orig_grey = Image_orig
edge, kernel = phase_stretch_transform(Image_orig_grey, LPF, S, W, Threshold_min,
Threshold_max, FLAG)
Overlay = mh.overlay(Image_orig_grey, Edge)
Edge = Edge.astype(np.uint8)*255
plt.imshow(Edge)
plt.show()
def main():
Image_orig = mh.imread("../data/cameraman.tif")
if Image_orig.ndim == 3:
Image_orig_grey = mh.colors.rgb2grey(Image_orig)
else:
Image_orig_grey = Image_orig
# 调用前面的函数，对图像进行相位拉伸变换，
edge, kernel = phase_stretch_transform(Image_orig_grey, LPF,
Phase_strength, Warp_strength,
Threshold_min, Threshold_max,
Morph_flag)
# 显示图像，
Overlay = mh.overlay(Image_orig_grey, edge)
edge = edge.astype(np.uint8) * 255
plt.imshow(edge)
plt.show()
dnat = dnaf > T
pylab.gray()
nuclei1 = dnat
pylab.imshow(dnat)
pylab.show()
#labelling thereshold image
labeled, nr_objects = mh.label(dnat)
print nr_objects # output number of objects
pylab.imshow(labeled)
pylab.jet() # makes image colourful
pylab.show()
dnaf = mh.gaussian_filter(dnaf, 8)
rmax = mh.regmax(dnaf)
pylab.imshow(mh.overlay(
dna, rmax)) # print dna and rmax (with second channel in red)
pylab.show() # seeds only show when image is zoomed in
dnaf = mh.gaussian_filter(dnaf,
16) # apply different filter to yield better result
rmax = mh.regmax(dnaf)
pylab.imshow(mh.overlay(dna, rmax))
pylab.show()
seeds, nr_nuclei = mh.label(rmax) # nuclei count
print nr_nuclei # unlike the example, the result is 36 compared to 22
dist = mh.distance(dnat)
dist = dist.max() - dist
dist -= dist.min()
dist = dist / float(dist.ptp()) * 255
fillvalue=''):
ax.imshow(img, **kwargs)
ax.set_title(label)
# show the original image and detected edges
print(' Original Image Edge Detected using PST')
imshow_pair((Image_orig, Edge), cmap='gray')
# Save results
default_directory, filename = filepath.split('./Test_Images/')
filename, extension = filename.split('.')
output_path = default_directory + './Test_Images/' + filename + '_edge.tif' # Saving the edge map with the extension tiff
mh.imsave(output_path, Edge)
else:
Overlay = mh.overlay(Image_orig_grey, Edge)
# Display results
def imshow_pair(image_pair, titles=('', ''), figsize=(10, 6), **kwargs):
fig, axes = plt.subplots(ncols=len(image_pair), figsize=figsize)
for ax, img, label in zip_longest(axes.ravel(),
image_pair,
titles,
fillvalue=''):
ax.imshow(img, **kwargs)
ax.set_title(label)
# show the original image, detected edges and an overlay of the original image with detected edges
print(
' Original Image Edge Detected using PST Overlay'
)
import pylab # Dio od matplotlib.
import mahotas as mh
import cv2
image = cv2.imread('pictures/sky.png',
0) # Ucitavamo sliku u grayscale prikazu.
filtered = mh.gaussian_filter(
image, 10) # Koristimo gauss filter za izostravanje slike.
result = filtered.astype(
'uint8'
) # Funckija mh.gaussian_filter() postavlja vrijednost varijable u float64.
rmax = mh.regmax(result) # Trazi maksimalne vrijednosti.
print rmax
pylab.imshow(mh.overlay(image, rmax))
pylab.show()
labeled, nr_objects = mh.label(rmax)
# mh.overlay() funckija postavlja img kao pozadinu a preko nje postavlja vrijednosti variable rmax u crvenom kanalu.
print('Broj pronadenih objekata je {}.'.format(nr_objects))
pylab.imshow(labeled)
# pylab.gray()
pylab.show()
dist = mh.distance(result)
dist = dist.max() - dist
dist -= dist.min()
dist = dist / float(dist.ptp()) * 255
dist = dist.astype(np.uint8)
pen = mh.imread('images/Penguins.jpg')
#pylab.imshow(pen)
#pylab.show()
# dnaf = mh.gaussian_filter(dna, 8)
#change type to int for otsu to work
# dnaf = dnaf.astype('uint8')
# T = mh.thresholding.otsu(dnaf)
# pylab.imshow(dnaf > T)
# pylab.show()
dnaf = mh.gaussian_filter(dna, 8)
rmax = mh.regmax(dnaf)
pylab.imshow(mh.overlay(dna, rmax))
pylab.show()
pylab.imshow(dnaf)
pylab.show()
# labeled, nr_objects = mh.label(dnaf > T)
# print "nr_objects: {}".format(nr_objects)
# pylab.imshow(labeled)
# pylab.jet()
# pylab.show()
# pylab.imshow(dna)
# pylab.show()
print dna.shape
print dna.dtype
# Luis Pedro Coelho Mahotas: Open source software for scriptable computer vision in Journal of Open Research Software, vol 1, 2013. [DOI]
# mahotas.citation()
#### MAIN ####
# Laden der Bilder
dna = mh.imread("dna.jpeg")
# dna = mh.imread("nicolic_1.tif")
nuc = mh.demos.nuclear_image()
# Gaussfiltern und Tresholding
dnaf = mh.gaussian_filter(dna, 16)
# mahotas.thresholding() braucht "uint8"
dnaf = dnaf.astype("uint8")
T = mh.thresholding.otsu(dnaf)
# Zählen der Objekte, Ausgabe in int und Plottbaren Array mit nummerierten Flecken
labeled, nr_objects = mh.label(dnaf > T)
print nr_objects, labeled.shape, labeled.max()
# Seeds finden
rmax=mh.regmax(dnaf)
# pylab.imshow(rmax)
pylab.imshow(mh.overlay(dna, rmax))
# pylab.imshow(labeled)
# pylab.imshow(dnaf > T)
# pylab.imshow(nuc)
pylab.jet()
pylab.show()
def measure(origimage, cuts, gaussintensity, iteration):
image = cut(origimage, cuts)
adjustsize(origimage, image, cuts)
if not default and iteration in modifyselection:
limitmod = modifyselection[iteration]
else:
limitmod = 0
ignore = findrim(image, limitmod)
if rim and len(ignore) <= (cuts**2) * 0.33:
while len(ignore) <= (cuts**2) * 0.33:
limitmod += 3
ignore = findrim(image, limitmod)
elif len(ignore) >= (cuts**2) * 0.66:
while len(ignore) >= (cuts**2) * 0.66:
limitmod -= 3
ignore = findrim(image, limitmod)
avmod = len(ignore)
threshold = applyth(image)
if not default and iteration in increasegauss:
usedgaussintensity = gaussintensity**increasegauss[iteration]
else:
usedgaussintensity = gaussintensity
gauss = applygauss(image, usedgaussintensity)
totalcellarea = 0.0
subcount = int(math.sqrt(len(image)))
for r in range(len(image)):
printprogress(
(iteration * len(image) + r + 1), (len(inputimages) * len(image)),
prefix="processing " + str(len(inputimages) - len(badimages)) +
" images",
suffix="complete")
if len(ignore) == 0 or r != ignore[0]:
cellarea = 0.0
area = image[r].shape[0] * image[r].shape[1]
gaussth = gauss[r] > threshold[r]
for array in gaussth:
for pixel in array:
# variabel machen wenn Zellen hell
if pixel == 0:
cellarea += 1
cellperc = (cellarea / area)
inputimages[iteration][3] = cellperc * 100
rmin = mh.regmin(gaussth)
rmin = rmin.astype(np.uint8)
plt.subplot(subcount, subcount, r + 1)
plt.axis('off')
plt.imshow(mh.overlay(image[r], rmin))
totalcellarea += cellperc
else:
plt.subplot(subcount, subcount, r + 1)
plt.axis('off')
ignore.pop(0)
plt.text(
-7, 7,
str("ID: " + str(iteration) + ", day: " +
str(inputimages[iteration][1]) + ", " + str(substancename) +
" concentration: " + str(inputimages[iteration][2]) + " %"))
plt.text(
-7, 6.5,
str(cellname) + " coverage: " +
str(round((totalcellarea / ((len(image)) - avmod)) * 100, 2)) + " %")
plt.axis('off')
pp.savefig()
plt.gcf().clear()
import matplotlib.pylab as pyl
import mahotas as mh
# Circles
# Read and show image
circles = misc.imread('circles.png')
pyl.imshow(circles)
pyl.show()
#Threshold
T = mh.otsu(circles)
circlesTh = (circles > T)
circlesF = mh.gaussian_filter(circles, 33)
circlesRegmax = mh.regmax(circlesF)
labels, near = mh.label(circlesRegmax)
pyl.imshow(mh.overlay(circles, circlesRegmax))
pyl.show()
#Count circles
km, circlesCnt = mh.label(circlesRegmax)
print circlesCnt
#Find center points
centerMass = mh.center_of_mass(circles, labels)[1:]
print centerMass
#Objects
objects = mh.imread('objects.png')
pyl.imshow(objects)
pyl.show()
def PST(I,
LPF=0.21,
Phase_strength=0.48,
Warp_strength=12.14,
Threshold_min=-1,
Threshold_max=0.0019,
Morph_flag=1):
# I: image
# Gaussian Low Pass Filter
# LPF = 0.21
# PST parameters:
# Phase_strength = 0.48
# Warp_strength = 12.14
# Thresholding parameters (for post processing after the edge is computed)
# Threshold_min = -1
# Threshold_max = 0.0019
# To compute analog edge, set Morph_flag = 0 and to compute digital edge, set Morph_flag = 1
# Morph_flag = 1
I_initial = I
if (len(I.shape) == 3):
I = I.mean(axis=2)
L = 0.5
x = np.linspace(-L, L, I.shape[0])
y = np.linspace(-L, L, I.shape[1])
[X1, Y1] = (np.meshgrid(x, y))
X = X1.T
Y = Y1.T
[THETA, RHO] = cart2pol(X, Y)
# Apply localization kernel to the original image to reduce noise
Image_orig_f = ((np.fft.fft2(I)))
expo = np.fft.fftshift(
np.exp(-np.power((np.divide(RHO, math.sqrt((LPF**2) /
np.log(2)))), 2)))
Image_orig_filtered = np.real(
np.fft.ifft2((np.multiply(Image_orig_f, expo))))
# Constructing the PST Kernel
PST_Kernel_1 = np.multiply(
np.dot(RHO, Warp_strength), np.arctan(np.dot(RHO, Warp_strength))
) - 0.5 * np.log(1 + np.power(np.dot(RHO, Warp_strength), 2))
PST_Kernel = PST_Kernel_1 / np.max(PST_Kernel_1) * Phase_strength
# Apply the PST Kernel
temp = np.multiply(np.fft.fftshift(np.exp(-1j * PST_Kernel)),
np.fft.fft2(Image_orig_filtered))
Image_orig_filtered_PST = np.fft.ifft2(temp)
# Calculate phase of the transformed image
PHI_features = np.angle(Image_orig_filtered_PST)
if Morph_flag == 0:
out = PHI_features
return out
else:
# find image sharp transitions by thresholding the phase
features = np.zeros((PHI_features.shape[0], PHI_features.shape[1]))
features[PHI_features > Threshold_max] = 1 # Bi-threshold decision
features[
PHI_features <
Threshold_min] = 1 # as the output phase has both positive and negative values
features[I < (
np.amax(I) / 20
)] = 0 # Removing edges in the very dark areas of the image (noise)
# apply binary morphological operations to clean the transformed image
out = features
out = mh.thin(out, 1)
out = mh.bwperim(out, 4)
out = mh.thin(out, 1)
out = mh.erode(out, np.ones((1, 1)))
Overlay = mh.overlay(I, out)
return (out, Overlay)
np.fft.fft2(Image_orig_filtered))
Image_orig_filtered_PST = np.fft.ifft2(temp)
# Calculate phase of the transformed image
PHI_features = np.angle(Image_orig_filtered_PST)
# find image sharp transitions by thresholding the phase
features = np.zeros((PHI_features.shape[0], PHI_features.shape[1]))
features[PHI_features > Threshold_max] = 1 # Bi-threshold decision
features[
PHI_features <
Threshold_min] = 1 # as the output phase has both positive and negative values
features[image < (
np.amax(image) /
20)] = 0 # Removing edges in the very dark areas of the image (noise)
# apply binary morphological operations to clean the transformed image
out = features
out = mh.thin(out, 1)
out = mh.bwperim(out, 4)
out = mh.thin(out, 1)
out = mh.erode(out, np.ones((1, 1)))
overlay = mh.overlay(image, out)
plt.title('Overlay of original image with PST detected edges:\n' + ', LPF = ' +
str(LPF) + ', phase_str = ' + str(Phase_strength) + ', warp_str = ' +
str(Warp_strength) + ',\n t_min = ' + str(Threshold_min) +
', t_max = ' + str(Threshold_max))
plt.imshow(overlay)
plt.show()
fillvalue=''):
ax.imshow(img, **kwargs)
ax.set_title(label)
# show the original image and detected edges
print(' Original Image Edge Detected using PST')
imshow_pair((Image_orig, Edge), cmap='gray')
# Save results
default_directory, filename = filepath.split('./Test_Images/')
filename, extension = filename.split('.')
output_path = default_directory + './Test_Images/' + filename + '_edge.tif' # Saving the edge map with the extension tiff
mh.imsave(output_path, Edge)
else:
Overlay = mh.overlay(Image_orig, Edge)
# Display results
def imshow_pair(image_pair, titles=('', ''), figsize=(10, 6), **kwargs):
fig, axes = plt.subplots(ncols=len(image_pair), figsize=figsize)
for ax, img, label in zip_longest(axes.ravel(),
image_pair,
titles,
fillvalue=''):
ax.imshow(img, **kwargs)
ax.set_title(label)
# show the original image, detected edges and an overlay of the original image with detected edges
print(
' Original Image Edge Detected using PST Overlay'
)
import numpy as np
import pylab # Dio od matplotlib.
import mahotas as mh
import cv2
image = cv2.imread('pictures/sky.png', 0) # Ucitavamo sliku u grayscale prikazu.
filtered = mh.gaussian_filter(image, 10) # Koristimo gauss filter za izostravanje slike.
result = filtered.astype('uint8') # Funckija mh.gaussian_filter() postavlja vrijednost varijable u float64.
rmax = mh.regmax(result) # Trazi maksimalne vrijednosti.
print rmax
pylab.imshow(mh.overlay(image, rmax))
pylab.show()
labeled, nr_objects = mh.label(rmax)
# mh.overlay() funckija postavlja img kao pozadinu a preko nje postavlja vrijednosti variable rmax u crvenom kanalu.
print ('Broj pronadenih objekata je {}.'.format(nr_objects))
pylab.imshow(labeled)
# pylab.gray()
pylab.show()
dist = mh.distance(result)
dist = dist.max() - dist
dist -= dist.min()
dist = dist/float(dist.ptp()) * 255
dist = dist.astype(np.uint8)
objects = mh.cwatershed(dist, labeled)
whole = mh.segmentation.gvoronoi(objects) # Voronoi segemtacija, svaki piksel poprima vrijednost najblizeg maksimuma.
pylab.imshow(objects)